49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import PIL.Image
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import PIL.ImageOps
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from packaging import version
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from PIL import Image
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if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
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PIL_INTERPOLATION = {
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"linear": PIL.Image.Resampling.BILINEAR,
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"bilinear": PIL.Image.Resampling.BILINEAR,
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"bicubic": PIL.Image.Resampling.BICUBIC,
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"lanczos": PIL.Image.Resampling.LANCZOS,
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"nearest": PIL.Image.Resampling.NEAREST,
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}
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else:
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PIL_INTERPOLATION = {
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"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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"nearest": PIL.Image.NEAREST,
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}
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def pt_to_pil(images):
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"""
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Convert a torch image to a PIL image.
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"""
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images = (images / 2 + 0.5).clamp(0, 1)
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images = images.cpu().permute(0, 2, 3, 1).float().numpy()
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images = numpy_to_pil(images)
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return images
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def numpy_to_pil(images):
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"""
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Convert a numpy image or a batch of images to a PIL image.
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"""
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if images.ndim == 3:
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images = images[None, ...]
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images = (images * 255).round().astype("uint8")
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if images.shape[-1] == 1:
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# special case for grayscale (single channel) images
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pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
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else:
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pil_images = [Image.fromarray(image) for image in images]
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return pil_images
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